Patch Feature Distribution. In Fig. REF , we visualize the patch features by t-SNE [1]} from OULU-NPU protocol 1, which both the training and testing sets consist of 5 patch types (live, print1, print2, screen1, screen2). We can observe in (b) that the model trained without margin cannot distinguish print1 and print2 types very well. The distribution for the live samples is more compact, and clusters are separated better for the model trained with the margin. The training and testing feature sets are aligned well in the feature space.
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